Related Experiment Video
Updated: Aug 6, 2026
Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
Published on: July 29, 2013
Evaluation of a CZT-based photon-counting detector CT prototype for low-dose lung cancer screening using
Kai Mei1, Leonid Roshkovan1, Sandra S Halliburton2
1Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background:
Photon-counting computed tomography (PCCT) is an advanced high-resolution, dose-efficient CT technique that can be beneficial for low-dose lung cancer screening (LCS-LDCT).
Objectives:
To evaluate the clinical performance of a cadmium-zinc-telluride- (CZT-) based PCCT system for LCS-LDCT using patient-specific 3D-printed lung phantoms, and to compare its image quality and radiomics consistency with a conventional energy-integrating detector CT (EIDCT) system.
Methods:
Six 3D-printed lung phantoms, derived from patient CT datasets and representing various lesion types (solid, part-solid, and ground-glass), were imaged on PCCT and EIDCT scanners at matched dose levels (1.6 - 20.4 mGy). Quantitative image metrics, Hounsfield unit (HU) accuracy, image noise, and contrast-to-noise ratio (CNR), were assessed across dose levels. Radiomic features were extracted for each lesion and analyzed via principal component analysis to quantify feature consistency (within-cluster distance) and lesion type separability.
Results:
PCCT demonstrated significantly lower image noise and higher CNR compared with EIDCT, particularly at lower dose levels. HU values were consistent across doses for both systems, with reduced variability in PCCT (coefficient of variation < 0.004). Radiomics analysis revealed tighter clustering (reduced within-cluster distances) and comparable lesion type separability between PCCT and EIDCT, indicating enhanced feature stability and lesion differentiation. Qualitative review confirmed superior lesion conspicuity and margin delineation with PCCT.
Conclusions:
CZT-based PCCT outperforms conventional EIDCT in quantitative and qualitative imaging metrics for LCS-LDCT, enabling superior image quality and radiomics reproducibility at reduced radiation doses. These findings support the clinical translation of PCCT for lung cancer screening and radiomics-based lesion characterization.
Clinical Impact:
PCCT offers reduced image noise and improved CNR performance, especially at ultra-low doses. PCCT may facilitate further dose reduction without compromising diagnostic accuracy in LCS-LDCT.
